Complexity, emergent (social) phenomena, Asimov’s “Psychohistory”

In honor of Isaac Asimov’s birthday, some thoughts about the fictional science of psychohistory, and how the later discovery of chaos, complexity, and emergent phenomena changed our understanding.

Isaac Asimov, from Wikimedia Commons CC0

Our understanding of chaos and complexity really didn’t start until we had computers powerful enough to simulate nonlinear equations with enough interactions with enough iterations; and we didn’t begin to have these computers until the 1960s. But when Isaac Asimov conceived his fictional science of “psychohistory” in 1942, we still didn’t realize how nonlinear phenomena become unpredictable over time.

In his Foundation series, Asimov posited the concept of “psychohistory”, which, within this fictional world, used complex equations to predict the overall shape of future human history. Asimov had said that he had conceived of this idea as a way of applying the concept of the ideal gas law (PV=nRT, where P is pressure, V is volume, n is the amount of gas in moles, R is the universal gas constant and T is temperature) to humanity.  While the ideal gas law cannot predict the movement or location of any individual gas atom, the law does predict how gases react to changes in pressure and temperature.

Happy birthday to Isaac Asimov, Grand Master of science fiction, author of more than 400 books, and biochemist.

So Asimov’s fictional concept was that you could do something similar with humans—you couldn’t predict what any individual person would do, but you could predict what collective Humanity would do; that individual actions wouldn’t have significant impacts on the collective pattern. And in Asimov’s fictional universe, this science’s founder, Hari Seldon, uses this to not only predict the fall of the Galactic Empire, but to chart out a shortened path to a new galactic renaissance.

Chaos, though, demonstrated that individual actions do have significant impacts on the overall pattern—small changes in initial conditions increase in magnitude over many iterations. The “butterfly effect” has become the clichéd image of this idea—the beating of a single butterfly’s wings creates changes in atmospheric currents that become amplified over time into a massive storm.

But where things get interesting is in the study of complexity—self-organization “on the edge of chaos”—that there are nonlinear systems that, rather than being entirely chaotic, form self-organizing patterns and structures. These phenomena include (among others) human social interactions, economics, social (in person and online) networks, and collective behavior patterns. M. Mitchell Waldrop defined this as, “where the components of a system never quite lock into place, and yet never quite dissolve into turbulence, either… the one place where a complex system can be spontaneous, adaptive, and alive.” [Waldrop, Complexity, 1992, page 12.]

SpringerOpen and BMC have a portfolio of journals focusing on exactly this, including Applied Network Science, Big Data Analytics, EPJ Data Science, and others. You can find a lot more information about the whole group of journals at

While the case remains that we cannot predict the future—complex phenomena remain unpredictable—it does seem that there are stable patterns of collective human behavior; which means that Asimov was both right and wrong. But in any case, happy birthday to Isaac Asimov, Grand Master of science fiction, author of more than 400 books, and biochemist.

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